Magnetic mineralogy of the Mercurian lithosphere
Bibliographic record
Abstract
Abstract Mercury and Earth are the only inner solar system planets with active, internally generated dynamo magnetic fields. The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission recently detected magnetic fields on Mercury that are consistent with lithospheric magnetization. We investigate the physical and chemical environment of Mercury's lithosphere, past and present, to establish the conditions under which magnetization may have been acquired and modified. Three factors are particularly crucial to the determination of crustal composition and iron mineralogy: redox conditions in the planet's crust and mantle, the iron content of the lithosphere, and, for any remanent magnetization, the temperature profile of the lithosphere and its evolution over time. We explore potential mechanisms for remanence acquisition and alteration on Mercury, whose surface environment is both hot and highly reducing. The long‐term thermal history of Mercury's crust plays an important role in the longevity of any remanent crustal magnetization, which may be subject to remagnetization through thermal, viscous, and shock mechanisms. This thermal and compositional framework is used both to constrain plausible candidate minerals that could carry magnetic remanence on Mercury and to evaluate their capacity to acquire and retain sufficient magnetization to be detectable from satellite orbit. We propose that iron metal and its alloys are likely to be the dominant contributors to induced and remanent magnetization in Mercury's lithosphere, with additional contributions from iron silicides, sulfides, and carbides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".